Abstract: Nowadays, smartphone users install dozens or even hundreds of Apps on their phones. Predicting App usage not only helps the mobile phone system to speed up App launching but also reduce the time for users to search App. This paper focuses on a novel session-based App usage prediction problem that tends to predict a sequence of Apps to be used in a period. A session-based embedding framework called SEM is proposed to solve the problem. Aiming at the side length of application session and the heterogeneity of session semantics, a session embedding method is proposed to form uniform feature representation, which alleviates the problem of user sparsity and obtains the vector representation of sessions. Based on session embedding, a two-layer GRU-based recursive neural network model is trained for App usage session prediction. Extensive experiments based on real datasets show that the proposed framework outperforms conventional App recommendation approaches.

Key words: App usage prediction, session-based embedding, recurrent neural network, gated recurrent unit